Genomes to fields 2024 maize genotype by environment prediction competition

Authors: CHEN, QIUYUE - University Of Wisconsin; Washburn, Jacob; LIMA, DAYANE CRISTINA - University Of Wisconsin; ROMAY, MARIA CINTA - Cornell University; GAGE, JOSEPH - North Carolina State University; Holland, James; XAVIER, ALENCAR - Corteva Agriscience; MURRAY, SETH - Texas A&M University; ERTL, DAVID - Iowa Corn Promotion Board; LOPEZ-CRUZ, MARCO - Michigan State University; DE LOS CAMPOS, GUSTAVO - Michigan State University; AGUATE, FERNANDO - Michigan State University; BEISSINGER, TIMOTHY - University Of Gottingen; BOHN, MARTIN - University Of Illinois Urbana-Champaign; Buckler Iv, Edward; Edwards, Jode; Flint Garcia, Sherry; GORE, MICHAEL - Cornell University; HIRSCH, CANDICE - University Of Minnesota; KAEPPLER, SHAWN - University Of Wisconsin; KEBEDE, AIDA - Agriculture And Agri-Food Canada; Knoll, Joseph; MCKAY, JOHN - Colorado State University; MINYO, RICHARD - The Ohio State University; ORTEZ, OSLER - The Ohio State University; RENEAU, JONATHAN - University Of Delaware; SCHNABLE, JAMES - University Of Nebraska; SEKHON, RAJANDEEP - Clemson University; SINGH, MANINDER - Michigan State University; SPARKS, ERIN - University Of Delaware; THOMPSON, ADDIE - Michigan State University; TUINSTRA, MITCHELL - Purdue University; WALLACE, JASON - University Of Georgia; XU, WENWEI - Texas A&M University; DE LEON, NATALIA - University Of Wisconsin

Submitted to: BMC Research Notes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/2/2026
Publication Date: 2/9/2026
Citation: Chen, Q., Washburn, J.D., Lima, D., Romay, M., Gage, J.L., Holland, J.B., Xavier, A., Murray, S.C., Ertl, D., Lopez-Cruz, M., De Los Campos, G., Aguate, F.M., Beissinger, T.M., Bohn, M.O., Buckler Iv, E.S., Edwards, J.W., Flint Garcia, S.A., Gore, M.A., Hirsch, C.N., Kaeppler, S.M., Kebede, A.Z., Knoll, J.E., Mckay, J.K., Minyo, R., Ortez, O.A., Reneau, J.W., Schnable, J.C., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thompson, A.M., Tuinstra, M.R., Wallace, J., Xu, W., De Leon, N. 2026. Genomes to fields 2024 maize genotype by environment prediction competition. BMC Research Notes. 19. https://doi.org/10.1186/s13104-026-07629-5.
DOI: https://doi.org/10.1186/s13104-026-07629-5

Interpretive Summary: Predicting crop yield is important to farmers, researchers, breeders, and the general public as it enables cost savings and in turn a more affordable food supply. However, yield prediction is difficult do to the many complicated factors involved as well as the lack of large public datasets for use in prediction model building. Here, a large dataset was generated and curated, and a prediction competition was held to invite researchers from across the world to use the data and create their best predictive models. Many potentially useful models were created and the dataset is available publicly for continued use in model development.

Technical Abstract: Objectives The Genomes to Fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (yearlocation combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.